My |

Created an interactive dashboard using Power BI to analyze employee data, including attrition rate, performance, satisfaction level, and department-wise insights. Enabled HR teams to make data-driven decisions through visualized metrics and filters.

Built a machine learning model to predict car prices using features like brand, model year, mileage, and engine specs. Applied data preprocessing, feature engineering, and regression techniques to improve prediction accuracy.

Developed a recommendation engine using collaborative and content-based filtering to suggest personalized movies. Incorporated data preprocessing, similarity metrics, and evaluation methods to fine-tune recommendation quality.

Built a FastAPI web service to estimate insurance premiums from client inputs like age, BMI, smoking status, and other risk factors. Utilized a machine learning model under the hood, with endpoints designed for easy integration or testing.

Developed a Machine Learning model to predict house prices in Karachi using regression techniques. Performed data preprocessing, feature engineering, and model evaluation to ensure accuracy. Built an interactive interface for users to input property details and get price predictions.

Developed a Machine Learning model to Classify an Email Spam or Not Spam , Performed data preprocessing, feature engineering, and model evaluation to ensure accuracy. Built an interactive interface for users to input for type an message to check Email Spam or Not,Use Naive Bayes Classifer for better accuracy

I developed a UK University Review Sentiment Analysis Dashboard using Machine Learning (XGBoost + TF-IDF) and deployed it with StreamlitPredicts sentiment (Negative, Neutral, Positive) from student reviews.Predicts ratings based on text feedback.Provides visual dashboards with graphs showing sentiment trends, top universities, and rating distributions